Improved invasive weed optimization based on clustering strategy

Abstract

Invasive weed optimization (IWO), which is inspired from the invasive behavior of weeds growth in nature, is a population-based intelligence algorithm. However, competitive exclusion may shrink search space and place most seeds in the same local area. Meanwhile, the accurate value of standard deviation is not easy to determine. These two shortcomings may lead to premature convergence and unable to achieve the global optimum. In order to overcome these two shortcomings, a clustering IWO (CIWO) is proposed by incorporating the core idea of clustering into IWO. We introduce a clustering strategy which is deployed before reproduction to disperse solution regions so that new seeds can locate in different areas. In addition, the value of standard deviation is based on statistical information and calculated from fittest individuals of each cluster so they can be accurate enough to the actual value and more representative. We compare it with the basic IWO, and a modified particle swarm optimization on a set of 14 benchmark functions. Experimental results indicate that CIWO is an effective and efficient algorithm can not only obtain the result superior to the standard invasive weed optimization but also explores and exploits the promising regions in the search space effectively.